基于免疫基因集的卵巢癌预后标志物的建立和验证。

Development and validation of an immune gene-set based Prognostic signature in ovarian cancer.

机构信息

State Key Laboratory of Reproductive Medicine, Nanjing Medical University, Nanjing 211166, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Jiangsu Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing 211166, China; Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, China.

National Health and Family Planning Commission Contraceptives Adverse Reaction Surveillance Center, Jiangsu Institute of Planned Parenthood Research, China.

出版信息

EBioMedicine. 2019 Feb;40:318-326. doi: 10.1016/j.ebiom.2018.12.054. Epub 2018 Dec 27.

DOI:10.1016/j.ebiom.2018.12.054

PMID:30594555

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6412087/

Abstract

BACKGROUND

Ovarian cancer (OV) is the most lethal gynecological cancer in women. We aim to develop a generalized, individualized immune prognostic signature that can stratify and predict overall survival for ovarian cancer.

METHODS

The gene expression profiles of ovarian cancer tumor tissue samples were collected from 17 public cohorts, including 2777 cases totally. Single sample gene set enrichment (ssGSEA) analysis was used for the immune genes from ImmPort database to develop an immune-based prognostic score for OV (IPSOV). The signature was trained and validated in six independent datasets (n = 519, 409, 606, 634, 415, 194).

FINDINGS

The IPSOV significantly stratified patients into low- and high-immune risk groups in the training set and in the 5 validation sets (HR range: 1.71 [95%CI: 1.32-2.19; P = 4.04 × 10] to 2.86 [95%CI: 1.72-4.74; P = 4.89 × 10]). Further, we compared IPSOV with nine reported ovarian cancer prognostic signatures as well as the clinical characteristics including stage, grade and debulking status. The IPSOV achieved the highest mean C-index (0.625) compared with the other signatures (0.516 to 0.602) and clinical characteristics (0.555 to 0.583). Further, we integrated IPSOV with stage, grade and debulking, which showed improved prognostic accuracy than clinical characteristics only.

INTERPRETATION

The proposed clinical-immune signature is a promising biomarker for estimating overall survival in ovarian cancer. Prospective studies are needed to further validate its analytical accuracy and test the clinical utility. FUND: This work was supported by National Key Research and Development Program of China, National Natural Science Foundation of China and Natural Science Foundation of the Jiangsu Higher Education Institutions of China.

摘要

背景

卵巢癌（OV）是女性中最致命的妇科癌症。我们旨在开发一种通用的、个体化的免疫预后标志物，以对卵巢癌进行分层和预测总生存期。

方法

从 17 个公共队列中收集了卵巢癌肿瘤组织样本的基因表达谱，总共包括 2777 例病例。使用 ImmPort 数据库中的免疫基因进行单样本基因集富集（ssGSEA）分析，以开发卵巢癌的基于免疫的预后评分（IPSOV）。该特征在 6 个独立数据集（n=519、409、606、634、415、194）中进行了训练和验证。

发现

在训练集和 5 个验证集中，IPSOV 显著将患者分为低免疫风险和高免疫风险组（HR 范围：1.71 [95%CI：1.32-2.19；P=4.04×10]至 2.86 [95%CI：1.72-4.74；P=4.89×10]）。此外，我们将 IPSOV 与 9 种已报道的卵巢癌预后标志物以及包括分期、分级和减瘤状态在内的临床特征进行了比较。与其他标志物（0.516 至 0.602）和临床特征（0.555 至 0.583）相比，IPSOV 实现了最高的平均 C 指数（0.625）。此外，我们将 IPSOV 与分期、分级和减瘤相结合，这表明其预后准确性优于仅基于临床特征。